GIS Data Collection. Remote Sensing

GIS Data Collection Remote Sensing

Data Collection Remote sensing Introduction Concepts Spectral signatures Resolutions: spectral, spatial, temporal Digital image processing (classification) Other systems Remote sensing and GIS Road map

Data Collection One of most expensive GIS activities Many diverse sources Two broad types of collection Data capture (direct collection) Data transfer Two broad capture methods Primary (direct measurement) Secondary (indirect derivation)

Primary Secondary Raster Digital remote sensing images (Image classification) Digital aerial photographs (Photogrammetry) Scanned maps or photos DEMs from contours Vector GPS measurements Survey measurements (COGO) On-screen digitizing of maps Toponymy data sets from atlases Data Collection Techniques

Surveying

Primary Data Capture Capture specifically for GIS use Raster remote sensing e.g. Landsat, SPOT and IKONOS satellites, and aerial photography (previously analog and vector, but increasingly digital and raster) Resolution is key consideration Spatial Spectral Temporal (Radiometric)

Remote Sensing is the measurement or acquisition of information of some property of an object or phenomena by a recording device that is not in physical or intimate contact with the object or phenomena under study Who has done remote sensing? Both a science (math and physics underlie the technology) and an art (image interpretation is not an immutable process)

Remote Sensing Includes UAV, aircraft, spacecraft and satellite-based systems Products can be analog (e.g., photos) or digital images Remotely sensed images need to be interpreted to yield thematic information (roads, crop lands, etc.) Yellow= Pine leading Orange = Fir leading Light Green = Pine and Fd type Dark Green = Fd, Spruce, Pine type Purple = Fd, Spruce, Aspen minor pine

Remote sensing applications Mapping Monitoring Modelling Global, continental, landscape, local (Scale is always an issue)

Important for updating large scale topographic maps (e.g., new roads, urban areas) Stereo-effect: pairs of images that are displaced produce a 3-D effect Allows for measuring elevation Much greater precision than satellite images Aerial photography

Synoptic coverage (study of inter-relations) Repetitive (enables monitoring of change) Multispectral imaging (beyond the visible region) Survey of inaccessible terrain Can also provide stereo coverage Advantages of Satellite R. S.

Satellite-based systems Data recorded for pixels (picture elements) Size on-ground of a pixel varies from <1m to 60m or more for commercial systems; up to 1km for many environmental monitoring systems Images are sent back from satellite as very large raster data sets MODIS image

Concepts: Sensor types Active or passive sensors Passive: sensors measure the amount of energy reflected (or emitted) from the earth s surface Active: sensor emits radiation in the direction of the target, it then detects and measures the radiation that is reflected or backscattered from the target.

Concepts (physics) Energy sources and radiation principles (e.g., Stefan- Boltzmann law, Wien s displacement law) Different sensors measure different parts of the electromagnetic spectrum Energy j EE TT 4 T k

0.4 0.5 0.6 0.7 µm UV blue red near infrared wavelength (µm) wavelength (µm) 10-6 10-5 10-4 10-3 10-2 10-1 1 10 10 2 10 3 10 4 10 5 10 6 10 7 TV and radio microwave thermal infrared mid infrared near infrared visible light Electromagnetic spectrum

The glue that holds it all together Electromagnetic Radiation Note that different disciplines reverse the order (placing Gamma Rays at the far left).

Source of energy Sensing system Earth s Surface A simple remote sensing system

EMR EMR EMR can be: EMR EMR 1: scattered 2: reflected 3: absorbed / emitted 4: transmitted EMR by an object Object-EMR Interactions

A: Source of EMR B: Electromagnetic radiation (EMR) C: Object of interest D: Sensor E: Transmission to receiver F: Data products G: Results of analyses A A complete remote sensing system

Primary blockers: Ozone, water vapour, CO 2 Theoretical EMR emissions Atmospheric windows

Spectral reflectance curves

Data Collection Remote sensing Introduction Concepts Spectral signatures Resolutions: spectral, spatial, temporal, radiometric Digital image processing (classification) Other systems Remote sensing and GIS Road map

Satellite-based sensors Landsat, SPOT, IKONOS, GeoEye,. also: Russian, Indian, Japanese, Chinese, European, and Canadian satellites They often differ in their spectral resolutions: Panchromatic versus multispectral What are the different types of spectral resolutions? New and planned systems have many more bands (hyperspectral images)

Spectral resolution # and width of bands determine how closely the spectral reflectance curve matches reality bands Multispectral vs Hyperspectral sensors

(shades of gray) # and width of bands determine how closely the spectral reflectance curve matches reality Spectral and Radiometric resolutions

Spatial resolution ~f(spectral region) Spatial resolution is typically tied to the extent of the area you wish to study. MODIS imagery (500 m) showing Myanmar before and after being hit by a cyclone. ASTER imagery 15 m VNIR 30 m SWIR 90 m TIR Fire scars north of Los Angeles

from IKONOS-1 1 m resolution Panchromatic image of the Jefferson memorial Interactive tools: pixel resolution Spatial Resolution Source: http://gis.washington.edu/cfr250/lessons/remote_sensing/

Pan sharpening

Geostationary Orbit: The satellite appears stationary with respect to the Earth's surface. Satellite orbits Earth observation satellites usually follow sun synchronous orbits. A sun synchronous orbit is a near-polar orbit whose altitude is such that the satellite will always pass over a location at a given latitude at the same local solar time. In this way, the same solar illumination condition (except for seasonal variation) can be achieved for the images of a given location taken by the satellite. Temporal resolution

Digital satellite data usually need considerable processing Registration and atmospheric correction Analysis: - measurement - classification - estimation Before correction The issue: we have spectral signatures derived from ground-based analyses, but the satellite data is top of the atmosphere data that is not necessarily equivalent. The result of atmospheric correction for a pixel from a grass field. After correction Digital image processing

True-color composite image (3, 2, 1) Near Infrared Composite (4,3,2) Shortwave Infrared Composite (7,4,3 or 7,4,2) Vegetation in the NIR band is highly reflective due to chlorophyll, and an NIR composite vividly shows vegetation in various shades of red. Water appears dark, almost black, due to the absorption of energy in the visible red and NIR bands. Reflectance in the SWIR region is due primarily to moisture content. SWIR bands are especially suited for camouflage detection, change detection, disturbed soils, soil type, and vegetation stress. Interactive tool: band combinations Image display

Temperature Vegetation biomass - Normalized Difference Vegetation Index (NDVI) Elevation Crop condition Urbanized area High (much vegetation) (little vegetation) Low Measurement

Identify and map areas with similar characteristics Assign meaningful categories such as land-use or landcover classes to pixel values Need training areas (ground-truth) Statistical approaches Classification Unsupervised vs supervised classes

Supervised Classification ESRI Image Classification

LODGEPOLE PINE LEADING DOUGLAS FIR LEADING MIXED / OTHERS Yellow= Pine leading Orange = Fir leading Light Green = Pine and Fd type Dark Green = Fd, Spruce, Pine type Purple = Fd, Spruce, Aspen minor pine Classification

Classification Reflectance varies with time of day Often large uncertainty in classification - pixels may contain several classes Mixed pixels Despite sophisticated image processing systems, good classification depends upon lots of experience (part art, part science) Effect of sun angle

Objective is to estimate total amounts of a quantity, or areas under cultivation for an administrative or management area Examples: crop areas, forest resources, drought monitoring JULY 2001 JULY 2002 LANDSAT THEMATIC MAPPER (SOURCE: CCRS 2002) Estimation

Other systems Meteorological satellites e.g., Advanced Very High Resolution Radiometer coarser resolution but higher frequency and larger areas covered designed for meteorology but used for many other purposes (e.g., NDVI) (News of the latest GOES satellite) Radar remote sensing (e.g., microwave) advantages in areas where cloud cover is frequent (e.g., tropical areas close to the equator) difficult to interpret http://www.geog.ucsb.edu/~jeff/wallpaper2/page.html

Other systems Aerial video visible light using off-the-shelf video cameras and post-processing systems cheap, rapid data collection for monitoring and data capture (http://www.draganfly.com)

Forest canopy (1st return) Using many rapid small bursts of laser light, an aircraft-borne apparatus records reflection from multiple sources. Ground surface (last return) Light Detection and Ranging

LIDAR Archaeology Oceanography

Socioeconomic applications Urban studies: e.g., delineation of newly urbanized areas (e.g., Quito, Manila) Demography: e.g., mapping of villages for population estimation (e.g., Sudan, W-Africa) with Landsat (rooftop surveys) Defense Meteorological Satellite Program s (DMSP) nighttime visible light emissions Human health and epidemiology: e.g., identify wetlands sources of malaria mosquitoes Archaeology and anthropology

Japan South-East Asia (water areas masked) DMSP data

Remote sensing and GIS Remotely sensed data is an important data source (currency, frequency) Large scale: e.g., cities revealed Medium scale: framework data, urban/non-urban, crop conditions, etc. Small scale: NDVI, global land cover data sets

Remote sensing and GIS Requires considerable processing to achieve high accuracy products Image rectification and registration with GIS data sets introduces uncertainty when working with raster systems (note: resampling types) Image interpretation guided by GIS data

Reference data Air photos Digital data Visual Digital Maps Statistics GIS data sets User Decision Maker Data products Interpretation Information products Target audience The remote sensing process

A summary of spatial and temporal resolutions associated with remote sensing systems. See your text for a full-sized illustration

Data Collection (expensive) Remote sensing Introduction Concepts (A/P, EMR) Spectral signatures Resolutions: spectral, spatial, temporal, radiometric Digital image processing (classification) Other systems Remote sensing and GIS Summary